Chemomechanical damage prediction from phase-field simulation video sequences using a deep-learning-based methodology
| Author(s) | Zeng, Quan | |
| Author(s) | Rezaei, Shahed | |
| Author(s) | Carrillo, Luis | |
| Author(s) | Davidson, Rachel | |
| Author(s) | Xu, Bai-Xiang | |
| Author(s) | Banerjee, Sarbajit | |
| Author(s) | Ding, Yu | |
| Date Accessioned | 2024-10-16T20:08:42Z | |
| Date Available | 2024-10-16T20:08:42Z | |
| Publication Date | 2024-09-12 | |
| Description | This article was originally published in iScience. The version of record is available at: https://doi.org/10.1016/j.isci.2024.110822. © 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | |
| Abstract | Highlights • Deep-learning-based method to predict chemo-mechanical processes in electrode materials • Prognostication of crack development and propagation based on machine learning • Potential to integrate in battery management systems of large-format batteries Summary Understanding the failure mechanisms of lithium-ion batteries is essential for their greater adoption in diverse formats. Operando X-ray and electron microscopy enable the evaluation of concentration, phase, and stress heterogeneities in electrode architectures. Phase-field models are commonly used to capture multi-physics coupling including the interplay between electrochemistry and mechanics. However, very little has been explored regarding developing predictive models that would forecast imminent failure. This study explores the application of convolutional long short-term memory networks for damage prediction in cathode materials using video sequence from phase-field simulations as a proxy for video microscopy. Two models are examined making use of, respectively, the damage video only and the damage and hydrostatic stress videos combined. We use customized quantitative metrics to compare the performance of the models. Our work demonstrates the outstanding capability of deep learning models using limited data to predict fracture behavior of battery materials, including crack propagation angle and length. Graphical abstract available at: https://doi.org/10.1016/j.isci.2024.110822 | |
| Sponsor | We acknowledge support from the National Science Foundation (NSF) Award CMMI2038625 as part of the NSF/DHS/DOT/NIH/USDA-NIFA Cyber-Physical Systems Program and CNS-2328395 as part of the Future Manufacturing program. We further acknowledge support of crystal growth under NSF DMR 1627197. | |
| Citation | Zeng, Quan, Shahed Rezaei, Luis Carrillo, Rachel Davidson, Bai-Xiang Xu, Sarbajit Banerjee, and Yu Ding. “Chemomechanical Damage Prediction from Phase-Field Simulation Video Sequences Using a Deep-Learning-Based Methodology.” iScience 27, no. 9 (September 20, 2024): 110822. https://doi.org/10.1016/j.isci.2024.110822. | |
| ISSN | 2589-0042 | |
| URL | https://udspace.udel.edu/handle/19716/35266 | |
| Language | en_US | |
| Publisher | iScience | |
| dc.rights | Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| Title | Chemomechanical damage prediction from phase-field simulation video sequences using a deep-learning-based methodology | |
| Type | Article |
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